Mr. Brett S. Sicard Profile

Brett S. Sicard

at McMaster Univ

SPIE Involvement:

Author

Publications (8)

Proceedings Article | 7 June 2024 Presentation + Paper

Generating synthetic data for data-driven solutions via a digital twin for condition monitoring in machine tools

Brett Sicard, Quade Butler, Yuandi Wu, Sepehr Abdolahi, Youssef Ziada, S. Andrew Gadsden

Proceedings Volume 13035, 1303503 (2024) https://doi.org/10.1117/12.3013119

KEYWORDS: Data modeling, Modeling, Systems modeling, Sensors, Manufacturing, Mathematical optimization

Read Abstract +

Proceedings Article | 7 June 2024 Presentation + Paper

Using estimation to optimize electric pump flow rates for spraying applications

Raveen Appuhamy, Alex McCafferty-Leroux, Brett Sicard, S. Andrew Gadsden

Proceedings Volume 13057, 1305703 (2024) https://doi.org/10.1117/12.3013959

KEYWORDS: Simulations, Control systems, Signal filtering, Nozzles, Simulink, Sensors, Fuzzy logic, Systems modeling, Photovoltaics, Manufacturing

Read Abstract +

Proceedings Article | 7 June 2024 Paper

Nonlinear filtering using the double exponential transformation

Quade Butler, Brett Sicard, Waleed Hilal, S. Andrew Gadsden, Youssef Ziada

Proceedings Volume 13057, 1305704 (2024) https://doi.org/10.1117/12.3013683

KEYWORDS: Tunable filters, Nonlinear filtering, Signal filtering, Digital filtering, Gaussian filters, Electronic filtering, Covariance, Monte Carlo methods, Design rules, Dynamical systems

Read Abstract +

Proceedings Article | 7 June 2024 Presentation + Paper

Aerospace digital twins: examining applications of digital twin technology to unmanned aerial vehicles and satellites

Brett Sicard, Alex McCafferty-Leroux, Raveen Appuhamy, Andrew Newton, Patrick Kosierb, Stephen Andrew Gadsden

Proceedings Volume 13052, 130520B (2024) https://doi.org/10.1117/12.3013115

KEYWORDS: Data modeling, Unmanned aerial vehicles, Satellites, Systems modeling, Modeling, Control systems, Prototyping, Navigation systems

Read Abstract +

Proceedings Article | 7 June 2024 Poster + Paper

Physics-informed neural networks for modeling hysteretic behavior in magnetorheological dampers

Yuandi Wu, Brett Sicard, Patrick Kosierb, Raveen Appuhamy, Alex McCafferty-Leroux, S. Andrew Gadsden

Proceedings Volume 13051, 130511G (2024) https://doi.org/10.1117/12.3012777

KEYWORDS: Data modeling, Neural networks, Systems modeling, Modeling, Mathematical modeling, Machine learning, Performance modeling, Deep learning, Artificial neural networks

Read Abstract +

In this article, the power of physics-informed neural networks is employed to address the issue of model identification for complex physical systems, focusing on the application of a magnetorheological (MR) damper setup. The research leverages the Bouc-Wen hysteresis model, a well-established representation of nonlinear behavior in MR dampers, to inform the training process of a series of cascaded neural networks. The core objective of this research is to develop a surrogate model capable of accurately predicting the dynamic behavior of MR dampers under various operational conditions. Traditionally, MR dampers pose significant modelling challenges due to their nonlinear and hysteresis-rich characteristics. The approach explored in this article combines physics-based insights with the capabilities of neural networks to resolve the complexity associated with the modelling process. The methodology involves the formulation of a physics-informed loss function, which embeds the Bouc-Wen hysteresis model’s governing equations into the training process of the neural networks. This fusion of physical principles and machine learning enables the networks to inherently capture the underlying physics, resulting in a more accurate and interpretable surrogate model. Through experimentation, the effectiveness of the physics-informed neural network approach in surrogate modeling for MR dampers is demonstrated. The model developed exhibits decent predictive performance across a range of input parameters and excitation conditions, offering a promising alternative to conventional black-box machine learning and physics-based methods. Furthermore, this research showcases the potential for physics-informed machine learning in modelling complex physical systems, offering a perspective on the utility of this approach in other engineering and scientific domains. The application of this methodology further facilitates improved control and optimization strategies in various engineering applications.

Proceedings Article | 14 June 2023 Presentation + Paper

Generalizing the unscented Kalman filter for state estimation

Quade Butler, Waleed Hilal, Brett Sicard, Youssef Ziada, S. Andrew Gadsden

Proceedings Volume 12547, 1254703 (2023) https://doi.org/10.1117/12.2664227

KEYWORDS: Signal filtering, Covariance, Error analysis, Tunable filters, Nonlinear filtering, Simulations, Complex systems, Systems modeling, Matrices, Covariance matrices

Read Abstract +

Proceedings Article | 14 June 2023 Presentation + Paper

Cognitive dynamic digital twin: enhancements for digital twin platforms based on human cognition

Brett Sicard, Quade Butler, Youssef Ziada, S. Andrew Gadsden

Proceedings Volume 12522, 125220B (2023) https://doi.org/10.1117/12.2664017

KEYWORDS: Data modeling, Artificial neural networks, Cognition, Artificial intelligence, Sensors, Feature extraction, Vibration, Systems modeling, Manufacturing, Modeling

Read Abstract +

Proceedings Article | 8 June 2022 Paper

Rapid parameter estimation of CNC feed drive systems

Quade Butler, Brett Sicard, Ethan Hughey, Youssef Ziada, David Stephenson, S. A. Gadsden

Proceedings Volume 12122, 121221B (2022) https://doi.org/10.1117/12.2626801

KEYWORDS: Manufacturing, Data modeling, Modeling, Error analysis, Data analysis, Servomechanisms, Performance modeling

Read Abstract +

Showing 5 of 8 publications

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years